### Table 5.18: Impact du nombre de stations de base potentielles sur le dimensionnement pour le profll de traflc 1 (60 sessions)

### Table 4: -chain data

1994

"... In PAGE 16: ... 5.2 Data for the -chain Approach The space required for the -chain method, relative to the number of symbols in the program, is shown in Table4 . Note that if a symbol is referenced (used or de ned) only at the Entry block, it will require no -operators.... ..."

Cited by 3

### Table 4: -chain data

1994

"... In PAGE 16: ... 5.2 Data for the -chain Approach The space required for the -chain method, relative to the number of symbols in the program, is shown in Table4 . Note that if a symbol is referenced (used or de ned) only at the Entry block, it will require no -operators.... ..."

Cited by 3

### Table 2. In uence du niveau de bruit additif sur F1b (moyenne sur 20 runs de 200000 evaluations). Les r esultats du bruit multiplicatif se r ev elent tr es d ecevants, et plafonnent a 2:27 dans les m^emes conditions. Le fait que les r esultats soient am elior es par un fort taux de bruit additif est a rapprocher du fait que l apos; evolution autonome obtenait les m^emes r esultats que l apos; evolution par inhibitions sur F1b.

"... In PAGE 3: ...5 1 0.5 R(t+1) = (1 ? 0)Rt + 0dR Table2 : Individus et Individus virtuels Les individus virtuels fournissent par induction, des indications pour guider la mutation. Ainsi, d apos;apr es la base d apos;exemples X; Y; Z, et T, une cause possible de bonne performance etant bit1 = 1 ou bit2 = 1, on cherchera a muter ces bits dans X ; pratiquement, la strat egie consiste a se rapprocher de H, ou imiter le h eros.... In PAGE 3: ... La strat egie d apos; evolution des individus est alors d e nie par le couple ( H; R), d ecrivant son attitude face au h eros et au repoussoir. Nous avons donn e un nom aux di erentes strat egies possibles ( Table2 ) ; ainsi la strat egie imitant le h eros et fuyant le repoussoir est celle du Mouton ; celle qui consiste a fuir le repoussoir uniquement, (qui correspond a l apos; evolution par inhibitions), est celle du Peureux, etc.... ..."

### Table 1: Mesure de performance sur s equences synth etiques calibr ees

### Table 1. Comparison between the total numbers of operations for Algorithm 1 de- pending of the base extension method.

2003

"... In PAGE 7: ... For the sake of simplicity, we decided to discard solutions using an approxi- mate base conversion. We compare in Table1 the efficiency of Algorithm 1... In PAGE 7: ...2, if the basic operations are well orga- nized, the bottleneck of the system is not the operations themselves but the data transmission between the memory and the different basic cells. In Table1 , we took into account all basic operations in our comparison. The method of Shenoy et al.... ..."

Cited by 5

### Table 1. Comparison between the total numbers of operations for Algorithm 1 de- pending of the base extension method.

"... In PAGE 7: ... For the sake of simplicity, we decided to discard solutions using an approxi- mate base conversion. We compare in Table1 the efficiency of Algorithm 1... In PAGE 7: ...2, if the basic operations are well orga- nized, the bottleneck of the system is not the operations themselves but the data transmission between the memory and the different basic cells. In Table1 , we took into account all basic operations in our comparison. The method of Shenoy et al.... ..."

### Table 3: Summary of Random Tree algorithms. The top three are based on linear algebra, the bottom six on random walks, and the middle one combines both techniques. The A subscript appears in the running time and space requirements of the algebraic algorithms since the operations being counted are arithmetic. In the absence of information on the numerical stability of these algorithms, it may be advisable to use exact arithmetic. Of the random walk algorithms, the top three are in the passive setting and the bottom three are in the active setting. All Markov chain parameters (which are de ned in Table 2) refer not to the Markov chain on the space of trees, but to the random walk on the graph. Quantities with over-bars and tildes refer to G (no self-loops added) and e G (self-loops added to make the graph out-degree regular), respectively.

1998

"... In PAGE 12: ... This Markov chain is known to randomize in polynomial time [22] [24], but when it is specialized to sampling trees, the algorithm fails to be competitive with the other tree algorithms. Table3 summarizes this history. 2.... In PAGE 37: ... Open Problems The main open question is, how well can one solve the Random State and Random Tree problems in the active setting? In particular, is it possible to solve the Random State problem more quickly than the mean hitting time ? Aldous gives a lower bound [1], but it is not clear how these bounds compare. There are quite a few maximal elements in the partial order of algorithms for the Random Tree problem ( Table3 ); an algorithm that runs in time O( ) for general graphs (rather than time O(e )) would reduce this number to two. Perhaps further progress could be made by combining random walk and algebraic techniques as done in [54].... ..."

Cited by 70

### Table 3: Summary of Random Tree algorithms. The top three are based on linear algebra, the bottom six on random walks, and the middle one combines both techniques. The A subscript appears in the running time and space requirements of the algebraic algorithms since the operations being counted are arithmetic. In the absence of information on the numerical stability of these algorithms, it may be advisable to use exact arithmetic. Of the random walk algorithms, the top three are in the passive setting and the bottom three are in the active setting. All Markov chain parameters (which are de ned in Table 2) refer not to the Markov chain on the space of trees, but to the random walk on the graph. Quantities with over-bars and tildes refer to G (no self-loops added) and e G (self-loops added to make the graph out-degree regular), respectively.

1998

"... In PAGE 12: ... This Markov chain is known to randomize in polynomial time [22] [24], but when it is specialized to sampling trees, the algorithm fails to be competitive with the other tree algorithms. Table3 summarizes this history. 2.... In PAGE 37: ... Open Problems The main open question is, how well can one solve the Random State and Random Tree problems in the active setting? In particular, is it possible to solve the Random State problem more quickly than the mean hitting time ? Aldous gives a lower bound [1], but it is not clear how these bounds compare. There are quite a few maximal elements in the partial order of algorithms for the Random Tree problem ( Table3 ); an algorithm that runs in time O( ) for general graphs (rather than time O(e )) would reduce this number to two. Perhaps further progress could be made by combining random walk and algebraic techniques as done in [54].... ..."

Cited by 70

### Table 2. De nitions of Operations on Sets and Relations: Examples

"... In PAGE 4: ...ontext. Table 1 presents some examples of the test problems investigated in [9]. These test problems employ de ned concepts that are speci ed in a knowl- edge base of hierarchical theories that Leo has access to. Table2 gives the... In PAGE 4: ...athematical symbols [; \, etc., for some concepts like union, intersection, etc., and we also use in x notation. For instance, the de nition of union on sets in Table2 can be easily read in its more common mathematical representation... ..."